Conventional radiosurgery systems use forward treatment planning or inverse treatment planning to treat pathological anatomies (e.g., tumor, lesion, vascular malformation, nerve disorder, etc.) during radiation treatment (e.g., radiosurgery or radiotherapy). In forward treatment planning, a medical physicist determines how the radiation source (beams) will be configured to apply the desired radiation dose to a tumor and then calculates how much radiation will be absorbed by critical structures (i.e., vital organs) and other healthy tissue. In inverse treatment planning, in contrast to forward planning, the medical physicist specifies the minimum and maximum doses to the tumor and the maximum dose to other healthy tissues independently, and the treatment planning system then selects the direction, size, total number, and sometimes energy of the radiation source's beams in order to achieve the specified dose conditions. Conventional treatment planning systems are designed to import 3-D images from a diagnostic imaging source, for example, computerized x-ray tomography (CT) scans. CT scans provide an accurate three-dimensional model of a volume of interest (e.g., skull or other tumor-bearing portion of the body) generated from a collection of CT slices and, thereby, the volume requiring treatment can be visualized in three dimensions. The VOIs are usually represented as voxels (volume elements), similar to pixels in a 2-D image.
During inverse planning, once the software package has imported the CT scans, the medical physicist manually delineates the extent of a volume of interest (VOI) in the CT to delineate a structure to be targeted or avoided with respect to the administered radiation dose. That is, the radiation source's beams are positioned in a sequence calculated to localize the radiation dose into a VOI that conforms as closely as possible to the target requiring treatment, while avoiding exposure of nearby healthy tissue. Once the target (e.g., tumor) VOI has been defined, and the critical and soft tissue volumes have been specified, the responsible radiation oncologist or medical physicist specifies the minimum and maximum radiation doses to the target VOI, and the maximum dose to normal and critical healthy tissue. The software then produces an inverse treatment plan, relying on the positional capabilities of the radiation treatment system, to meet, as closely as possible, the min/max dose constraints of the treatment plan.
FIG. 1 is a conceptual illustration of a graphical output of a treatment planning system displaying a slice of a CT image. The illustration of the CT image includes a pathological anatomy that is targeted for treatment, as well as a critical region that is located near the pathological anatomy. Conventionally, a user manually delineates points (e.g., some of the dots on the contour lines of FIG. 1) on the display that is used by the treatment planning system to generate a contour around the critical region and a target region contour around the pathological anatomy. Based on specified minimum and maximum doses to the target region and the maximum dose to the critical region, the treatment planning system generates a dose isocontour for the target region (e.g., lines joining points of equal dose, expressed in absolute units, for example, 40 Gy, 50 Gy, etc., or as a percentage of a maximal or user defined dose, for example, 60%, 70%, 80%, etc.). Ideally, the isocontour of the desired dose to be delivered to the target should perfectly match the contour of the target region. In some cases, the dose isocontour generated by the treatment planning system is not optimal, and can include portions of the critical region, as illustrated in FIG. 1.
Two principal requirements for an effective radiation treatment system are dose conformality, and to a lesser extent, homogeneity. Homogeneity is the uniformity of the radiation dose over the volume of the target (e.g., pathological anatomy such as a tumor, lesion, vascular malformation, etc.) and can be characterized by a dose volume histogram (DVH). An ideal DVH for the pathological anatomy is often considered a rectangular function as illustrated in FIG. 2, where the dose is 100 percent of the prescribed dose over the entire volume of the pathological anatomy. A desirable DVH for a critical region would have the profile illustrated in FIG. 3, where the volume of the critical anatomical structures receives as little of the prescribed dose as possible.
Conformality is the degree to which the desired dose isocontour of the radiation dose distribution matches (conforms) to the shape and extent of the target (e.g., tumor) in order to minimize damage to critical adjacent structures. More specifically, conformality is a measure of the amount of prescription (Rx) dose (amount of dose applied) within a target VOI. Conformality may be measured using a conformality index (CI)=(total volume at >=Rx dose)/(target volume at >=Rx dose). Perfect conformality results in a CI=1.
The treatment planning process typically requires a user to employ a treatment planning software program to complete many treatment-planning objectives like target coverage, conformality and homogeneity of the dose distribution, treatment time, etc. In order to optimize the treatment plan, conventional treatment planning software programs group multiple treatment-planning objectives into a single mathematical cost function and optimize the entire cost function. By optimizing the single cost function, the treatment planning software program optimizes the multiple treatment-planning objectives together, simultaneously and collectively.
In one conventional approach to prioritizing one treatment-planning objective over another, a user specifies a weighting factor for each of the treatment-planning objectives. Yet, because the objectives are optimized simultaneously and collectively, there is limited control of the resulting trade-off between the multiple treatment-planning objectives. In addition, because the objectives are optimized simultaneously and collectively, the user may have to run this collective optimization process many times, assessing the treatment plan after each optimization iteration, and manually modifying parameters, such as the weighting factor or a dose constraint, in an attempt to improve the optimization. This manual and iterative optimization process requires time and user experience in changing the treatment-planning parameters to improve the optimization process. In addition, because treatment plans differ from patient to patient, as well as from treatment region to treatment region (e.g., cranial versus lung), the treatment planning system requires the user to manually input parameters throughout the treatment planning process for each different treatment plan. This manual process requires time, can result in less than optimal treatment plans, and has the potential to result in user-input errors.